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TensorFlow Training Courses

Local, instructor-led live TensorFlow training courses demonstrate through interactive discussion and hands-on practice how to use the TensorFlow system to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

TensorFlow training is available as "onsite live training" or "remote live training". Onsite live TensorFlow trainings in the Philippines can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.

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Testimonials

★★★★★

★★★★★

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus

Course: Neural Networks Fundamentals using TensorFlow as Example

I generally enjoyed the knowledgeable trainer.

Sridhar Voorakkara

Course: Neural Networks Fundamentals using TensorFlow as Example

I was amazed at the standard of this class - I would say that it was university standard.

David Relihan

Course: Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview. Good background into why Tensorflow operates as it does.

Kieran Conboy

Course: Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane

Course: Neural Networks Fundamentals using TensorFlow as Example

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee

Course: TensorFlow for Image Recognition

Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.

Commerzbank AG

Course: Neural Networks Fundamentals using TensorFlow as Example

I was benefit from topic selection. Style of training. Practice orientation.

Commerzbank AG

Course: Neural Networks Fundamentals using TensorFlow as Example

A wide range of topics covered and substantial knowledge of the leaders.

ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course: Understanding Deep Neural Networks

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Lack

ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course: Understanding Deep Neural Networks

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Big theoretical and practical knowledge of the lecturers. Communicativeness of trainers. During the course, you could ask questions and get satisfactory answers.

Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course: Understanding Deep Neural Networks

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Practical part, where we implemented algorithms. This allowed for a better understanding of the topic.

ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course: Understanding Deep Neural Networks

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exercises and examples implemented on them

Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course: Understanding Deep Neural Networks

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Examples and issues discussed.

ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course: Understanding Deep Neural Networks

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Substantive knowledge, commitment, a passionate way of transferring knowledge. Practical examples after a theoretical lecture.

Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course: Understanding Deep Neural Networks

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Practical exercises prepared by Mr. Maciej

ING Bank Śląski S.A.; Kamil Kurek Programowanie

Course: Understanding Deep Neural Networks

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TensorFlow Course Outlines

TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms- be able to carry out installation / production environment / architecture tasks and configuration- be able to assess code quality, perform debugging, monitoring- be able to implement advanced production like training models, building graphs and logging

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms- be able to carry out installation / production environment / architecture tasks and configuration- be able to assess code quality, perform debugging, monitoring- be able to implement advanced production like training models, embedding terms, building graphs and logging

The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision。

In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

By the end of the training, participants will be able to:

- Train various types of neural networks on large amounts of data.- Use TPUs to speed up the inference process by up to two orders of magnitude.- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

By the end of this training, participants will be able to:

- Explore how data is being interpreted by machine learning models- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.- Explore the properties of a specific embedding to understand the behavior of a model- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

TensorFlow Serving is a system for serving machine learning (ML) models to production.

In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

By the end of this training, participants will be able to:

- Train, export and serve various TensorFlow models- Test and deploy algorithms using a single architecture and set of APIs- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

-

have a good understanding on deep neural networks(DNN), CNN and RNN

-

understand TensorFlow’s structure and deployment mechanisms

-

be able to carry out installation / production environment / architecture tasks and configuration

-

be able to assess code quality, perform debugging, monitoring

-

be able to implement advanced production like training models, building graphs and logging

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.

Python is a popular programming language that contains libraries for Deep Learning for NLP.

Deep Learning for NLP (Natural Language Processing) allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos.

In this instructor-led, live training, participants will learn to use Python libraries for NLP as they create an application that processes a set of pictures and generates captions.

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